64 lines
1.9 KiB
Plaintext
64 lines
1.9 KiB
Plaintext
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Introduction
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============
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This tool provides a Python interface to LIBSVM with instance weight support
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Installation
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============
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Please check README for detail.
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USAGE
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=====
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The usage is bascally the same as the version without supporting
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instance weights. We only show differences below.
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- Function: svm_train
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There are three ways to call svm_train()
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>>> model = svm_train(W, y, x [, 'training_options'])
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>>> model = svm_train(prob [, 'training_options'])
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>>> model = svm_train(prob, param)
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W: a list/tuple of l training weights (type must be double).
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Use [] if no weights.
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y: a list/tuple of l training labels (type must be int/double).
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x: a list/tuple of l training instances. The feature vector of
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each training instance is an instance of list/tuple or dictionary.
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training_options: a string in the same form as that for LIBSVM command
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mode.
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prob: an svm_problem instance generated by calling
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svm_problem(W, y, x).
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param: an svm_parameter instance generated by calling
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svm_parameter('training_options')
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model: the returned svm_model instance. See svm.h for details of this
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structure. If '-v' is specified, cross validation is
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conducted and the returned model is just a scalar: cross-validation
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accuracy for classification and mean-squared error for regression.
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To train the same data many times with different
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parameters, the second and the third ways should be faster..
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Examples:
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>>> y, x = svm_read_problem('../heart_scale')
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>>> W = [1] * len(y)
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>>> W[0] = 10
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>>> prob = svm_problem(W, y, x)
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>>> param = svm_parameter('-s 3 -c 5 -h 0')
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>>> m = svm_train([], y, x, '-c 5')
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>>> m = svm_train(W, y, x)
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>>> m = svm_train(prob, '-t 2 -c 5')
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>>> m = svm_train(prob, param)
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>>> CV_ACC = svm_train(W, y, x, '-v 3')
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